District heating (DH) is an energy efficient building heating system that entails low primary energy consumption and reduced environmental impact. The estimation of the required heating load provides information for operators to control district heating systems (DHSs) efficiently. It also yields historical datasets for intelligent management applications. Based on the existing virtual sensor capabilities to estimate physical variables, performance, etc., and to detect the anomaly detection in building energy systems, this paper proposes a virtual sensor-based method for the estimation of DH energy consumption in a residential building. Practical issues, including sensor absences and limited datasets corresponding to actual buildings, were also analyzed to improve the applicability of virtual sensors in a building. According to certain virtual sensor development processes, model-driven, data-driven, and grey-box virtual sensors were developed and compared in a case study. The grey-box virtual sensor surpassed the capabilities of the other virtual sensors, particularly for operation patterns corresponding to low heating, which were different from those in the training dataset; notably, a 16% improvement was observed in the accuracy exhibited by the grey-box virtual sensor, as compared to that of the data-driven virtual sensor. The former sensor accounted for a significantly wider DHS operation range by overcoming training data dependency when estimating the actual DH energy consumption. Finally, the proposed virtual sensors can be applied for continuous commissioning, monitoring, and fault detection in the building, since they are developed based on the DH variables at the demand side.

Building energy savings and occupant thermal comfort are the main issues in building technology. As such, the development of energy-efficient heating, ventilation, and air-conditioning (HVAC) systems and the control strategies of HVAC systems are emerging as important topics in the HVAC industry. Variable refrigerant flow (VRF) systems have efficient energy performance, so the use of VRF systems in buildings is increasing. However, most studies on VRF systems focus on improving mechanical efficiency, with few studies on energy-efficient control while satisfying the thermal comfort of occupants. The goal is to estimate the energy-saving potential of adjusting the temperature set-points and dead-band (range) in VRF air-conditioned building. To do so, we analyzed the influence of control strategies of a VRF system on human thermal comfort and energy consumption using a simulation method. The results showed that energy consumption can be reduced by 25.4% for predicted mean vote (PMV)-based control and 27.0% for the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) comfort range control compared with the typical set-point temperature control of a VRF system. The indoor thermal environments of the analyzed control strategies are controlled in the thermal comfort range, which is based on a PMV at ±0.5. Compared with the typical set-point control, PMV and ASHRAE comfort range-based control reduced the operation time of the compressor in the VRF system.